Job Description
Research Scientist – Machine Learning for Wearables (Maternal & Digital Health)
Location: London Bridge (Hybrid, minimum 3–4 days on site, with regular presence in South East London)
Contract: 3-year academic contract with renewal
BioTalent has partnered with a leading academic research programme to appoint a Research Scientist focused on machine learning, wearable data, and digital health.
This is a genuinely research-led role, not a product or engineering position. You’ll be working at the intersection of healthcare, AI, and real-world data, applying deep learning to large-scale, longitudinal datasets to better understand health trajectories and enable personalised interventions.
The Opportunity
You’ll be part of a global research initiative analysing multimodal data from wearable devices and digital biomarkers, with a focus on maternal and early childhood health. The work centres on continuous physiological monitoring, including heart rate, heart rate variability, sleep, activity, and behavioural signals, alongside emerging modalities such as voice biomarkers.
This is a rare chance to apply machine learning in a setting where the output directly informs clinical understanding and real-world interventions, rather than just model optimisation.
What You’ll Be Doing
• Develop predictive machine learning models using multimodal wearable data
• Work with continuous physiological signals including heart rate, sleep, activity, and energy expenditure
• Apply deep learning and signal processing techniques to identify patterns and anomalies in longitudinal health data
• Integrate wearable data with clinical and behavioural markers such as blood pressure, glucose, and gestational metrics
• Explore emerging approaches such as voice biomarkers to detect physical and mental health signals
• Validate models for robustness, generalisability, and ethical use across diverse populations
• Build and maintain reproducible pipelines for data processing and feature engineering
• Collaborate with clinicians, statisticians, and researchers to ensure outputs are clinically meaningful
• Contribute to publications, conference presentations, and wider research outputs
What They’re Looking For
• PhD in Bioinformatics, Computer Science, Data Science, or a closely related field
• Strong experience in machine learning or deep learning applied to healthcare or biological data
• Hands-on experience working with wearable or time-series physiological data
• Solid programming skills, typically Python
• Experience with signal processing, feature extraction, or anomaly detection
• Track record of academic research, including publications or conference contributions
• Ability to translate complex modelling outputs into clear, usable insights
Nice to have:
• Experience with digital health, remote monitoring, or real-world data
• Familiarity with maternal health or clinical biomarkers
• Exposure to multimodal data integration
• Experience contributing to grant applications or funded research
Why This Role
• Work on one of the most ambitious digital health research programmes globally
• Apply machine learning to real-world clinical problems, not abstract datasets
• Access to large-scale, longitudinal wearable datasets
• Strong academic environment with opportunities for publication and conference exposure
• Long-term stability within a funded programme, despite academic contract structure